#Plot a Figure


import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))
    biases=tf.Variable(tf.zeros([1,out_size])+0.1) 
    #biases is recommended not 0
    Wx_plus_b=tf.matmul(inputs,Weights)+biases
    if activation_function is None:
      outputs=Wx_plus_b
    else:
      outputs=activation_function(Wx_plus_b)
    return outputs
    
x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise


xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])

#add hidden layer
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
#add input layer
prediction=add_layer(l1,10,1,activation_function=None)

#the error between prediction and real data
loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)


init=tf.initialize_all_variables()
sess=tf.Session()
sess.run(init)

#plot figure
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.show()



for i in range(1000):
  sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
  if i%50==0:

    #to see the step improvement
     print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
    
'''
     try:
         ax.lines.remove(lines[0])
     except Exception:
         pass
     prediction_value=sess.run(prediction,feed_dict={xs:x_data})
     lines=ax.plot(x_data,prediction_value,'r-',lw=5)
     ax.lines.remove(lines[0])
     plt.pause(0.1)
'''
